Encyclopedia of Machine LearningEditors: Claude Sammut, Geoffrey I. WebbISBN: 978-0-387-30768-8 (Print) 978-0-387-30164-8 (Online)
Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component.
Bruno Olshausen, UC BerkeleyFoundations of Machine Learning
ransform your features into a higher dimensional, sparse space. Then train a linear model on these features.First fit an ensemble of trees (totally random trees, a random forest, or gradient boosted trees) on the training set. Then each leaf of each tree in the ensemble is assigned a fixed arbitrary feature index in a new feature space. These leaf indices are then encoded in a one-hot fashion.Each sample goes through the decisions of each tree of the ensemble and ends up in one leaf per tree. The sample is encoded by setting feature values for these leaves to 1 and the other feature values to 0.The resulting transformer has then learned a supervised, sparse, high-dimensional categorical embedding of the data.
Built in spare time by @karpathy to accelerate research.
Basically a good way to keep up with recent research in ML
TL;DR for the AWS-savvy: Our image is cs231n_caffe_torch7_keras_lasagne_v2, AMI ID: ami-125b2c72 in the us-west-1 region. Use a g2.2xlarge instance. Caffe, Torch7, Theano, Keras and Lasagne are pre-installed. Python bindings of caffe are available. It has CUDA 7.5 and CuDNN v3.
About this course: Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI.
This is a bare bones example of TensorFlow, a machine learning package published by Google. You will not find a simpler introduction to it.
First, read fucking Hastie, Tibshirani, and whoever. Chapters 1-4 and 7. If you don’t understand it, keep reading it until you do.
You can read the rest of the book if you want. You probably should, but I’ll assume you know all of it.
Take Andrew Ng’s Coursera. Do all the exercises in Matlab and python and R. Make sure you get the same answers with all of them.
Now forget all of that and read the deep learning book. Put tensorflow or torch on a Linux box and run examples until you get it. Do stuff with CNNs and RNNs and just feed forward NNs.
Once you do all of that, go on arXiv and read the most recent useful papers. The literature changes every few months, so keep up.
There. Now you can probably be hired most places. If you need resume filler, so some Kaggle competitions. If you have debugging questions, use StackOverflow. If you have math questions, read more. If you have life questions, I have no idea.